Qwen3.5 9B

Qwen3.5 9B is an open-weight release from the Qwen family, distributed as a post-trained model with weights and configuration files in the Hugging Face Transformers format and compatibility with inference stacks such as vLLM, SGLang, and KTransformers. The creator describes Qwen3.5 as a unified vision-language foundation that uses early-fusion training over multimodal tokens, claiming cross-generational parity with Qwen3 and stronger results than Qwen3-VL on reasoning, coding, agents, and visual understanding benchmarks. The same release notes frame the family as a step forward in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility, with expanded coverage of 201 languages and dialects.

Under the hood, Qwen3.5 pairs Gated Delta Networks with a sparse Mixture-of-Experts design to deliver high-throughput inference with minimal latency overhead, and the creator points to near-100% multimodal training efficiency relative to text-only runs alongside asynchronous reinforcement learning infrastructure for agent training at scale. These traits position the 9B variant as a practical mid-size option for developers who want a single open-weight model that can handle visual understanding alongside text reasoning and tool use, rather than stitching together separate vision and language models. Deployment is straightforward across the usual open-source runtimes, and the same model is also packaged for ROCm-based AMD Inference Microservice serving on Instinct, Radeon Pro, and EPYC hardware with an OpenAI-compatible API, broadening the environments where it can be put to work.